where \(\odot \)  refers to element-wise multiplication and \(W_h\) , \(W_c\) , \(b_h\)  and \(b_c\)  are learnable parameters. The Generator of the proposed architecture is shown in the left portion of Figure REF . At each time-step \(t\) , \(\tilde{G}\)  generates \(\tilde{y}_t\)  and \(\hat{G}\)  generates \(\hat{y}_t\) . The parameters for the distribution of offsets are estimated from \(\tilde{y}_t\)  while those for the distribution of pen-states are estimated from \(\hat{y}_t\)  as given in [1]}.
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